COMPANY BANKRUPTCY ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI
About this ebook
To grasp the dataset's characteristics, we delved into categorized features' distributions, allowing us to understand the underlying patterns within the data. This step helped us gain insights into the distribution of attributes across different classes, aiding in feature selection and engineering.
Moving on to the heart of our project, the prediction of company bankruptcy, we employed various machine learning models. Utilizing grid search, we performed hyperparameter tuning to optimize model performance. Our model arsenal included Logistic Regression, K-Nearest Neighbors, Support Vector, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), which were evaluated using accuracy, precision, recall, and F1-score.
Transitioning to deep learning, we implemented an Artificial Neural Network (ANN) model. This involved constructing a feed-forward neural network with hidden layers, dropouts, and activation functions. We evaluated the ANN using accuracy, precision, recall, and F1-score, gaining a comprehensive understanding of its classification performance.
Our journey into deep learning continued with the implementation of Long Short-Term Memory (LSTM) networks, which are well-suited for sequence data. We structured the LSTM model with multiple layers and dropouts, evaluating its performance using metrics like accuracy, precision, recall, and F1-score. This marked a pivotal step in predicting company bankruptcy.
Furthermore, we explored Feed-Forward Neural Networks (FNN) for prediction. Constructing a multi-layered architecture with varied dropouts and activation functions, we assessed its classification capabilities using metrics similar to previous models.
Incorporating Recurrent Neural Networks (RNN) added another dimension to our analysis. Building an RNN model with sequential data, we examined its accuracy, precision, recall, and F1-score, highlighting its ability to capture sequential patterns in bankruptcy data.
To comprehensively evaluate our models, we employed a range of metrics including precision, recall, F1-score, and accuracy. These metrics enabled us to gauge not only the overall model performance but also its capability to correctly predict bankrupt and non-bankrupt cases.
Our project also extended into creating a Python GUI using PyQt. This graphical interface facilitated user interaction, allowing them to input data for prediction and view the outcomes through an intuitive interface. This GUI enhanced accessibility and usability, making it easier for users to engage with our models.
In conclusion, our journey through the "Company Bankruptcy Analysis and Prediction Using Machine Learning with Python GUI" project encompassed data exploration, categorized features distribution analysis, model selection, performance evaluation using diverse metrics, and the creation of an interactive GUI. This endeavor combined analytical rigor, machine learning expertise, and user-centric design to provide a comprehensive solution for predicting company bankruptcy.
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About the author
Vivian Siahaan is a fast-learner who likes to do new things. She was born, raised in Hinalang Bagasan, Balige, on the banks of Lake Toba, and completed high school education from SMAN 1 Balige. She started herself learning Java, Android, JavaScript, CSS, C ++, Python, R, Visual Basic, Visual C #, MATLAB, Mathematica, PHP, JSP, MySQL, SQL Server, Oracle, Access, and other programming languages. She studied programming from scratch, starting with the most basic syntax and logic, by building several simple and applicable GUI applications. Animation and games are fields of programming that are interests that she always wants to develop. Besides studying mathematical logic and programming, the author also has the pleasure of reading novels. Vivian Siahaan has written dozens of ebooks that have been published on Sparta Publisher: Data Structure with Java; Java Programming: Cookbook; C ++ Programming: Cookbook; C Programming For High Schools / Vocational Schools and Students; Java Programming for SMA / SMK; Java Tutorial: GUI, Graphics and Animation; Visual Basic Programming: From A to Z; Java Programming for Animation and Games; C # Programming for SMA / SMK and Students; MATLAB For Students and Researchers; Graphics in JavaScript: Quick Learning Series; JavaScript Image Processing Methods: From A to Z; Java GUI Case Study: AWT & Swing; Basic CSS and JavaScript; PHP / MySQL Programming: Cookbook; Visual Basic: Cookbook; C ++ Programming for High Schools / Vocational Schools and Students; Concepts and Practices of C ++; PHP / MySQL For Students; C # Programming: From A to Z; Visual Basic for SMA / SMK and Students; C # .NET and SQL Server for High School / Vocational School and Students. At the ANDI Yogyakarta publisher, Vivian Siahaan also wrote a number of books including: Python Programming Theory and Practice; Python GUI Programming; Python GUI and Database; Build From Zero School Database Management System In Python / MySQL; Database Management System in Python / MySQL; Python / MySQL For Management Systems of Criminal Track Record Database; Java / MySQL For Management Systems of Criminal Track Records Database; Database and Cryptography Using Java / MySQL; Build From Zero School Database Management System With Java / MySQL.
Rismon Hasiholan Sianipar was born in Pematang Siantar, in 1994. After graduating from SMAN 3 Pematang Siantar 3, the writer traveled to the city of Jogjakarta. In 1998 and 2001 the author completed his Bachelor of Engineering (S.T) and Master of Engineering (M.T) education in the Electrical Engineering of Gadjah Mada University, under the guidance of Prof. Dr. Adhi Soesanto and Prof. Dr. Thomas Sri Widodo, focusing on research on non-stationary signals by analyzing their energy using time-frequency maps. Because of its non-stationary nature, the distribution of signal energy becomes very dynamic on a time-frequency map. By mapping the distribution of energy in the time-frequency field using discrete wavelet transformations, one can design non-linear filters so that they can analyze the pattern of the data contained in it. In 2003, the author received a Monbukagakusho scholarship from the Japanese Government. In 2005 and 2008, he completed his Master of Engineering (M.Eng) and Doctor of Engineering (Dr.Eng) education at Yamaguchi University, under the guidance of Prof. Dr. Hidetoshi Miike. Both the master's thesis and his doctoral thesis, R.H. Sianipar combines SR-FHN (Stochastic Resonance Fitzhugh-Nagumo) filter strength with cryptosystem ECC (elliptic curve cryptography) 4096-bit both to suppress noise in digital images and digital video and maintain its authenticity. The results of this study have been documented in international scientific journals and officially patented in Japan. One of the patents was published in Japan with a registration number 2008-009549. He is active in collaborating with several universities and research institutions in Japan, particularly in the fields of cryptography, cryptanalysis and audio / image / video digital forensics. R.H. Sianipar also has experience in conducting code-breaking methods (cryptanalysis) on a number of intelligence data that are the object of research studies in Japan. R.H. Sianipar has a number of Japanese patents, and has written a number of national / international scientific articles, and dozens of national books. R.H. Sianipar has also participated in a number of workshops related to cryptography, cryptanalysis, digital watermarking, and digital forensics. In a number of workshops, R.H. Sianipar helps Prof. Hidetoshi Miike to create applications related to digital image / video processing, steganography, cryptography, watermarking, non-linear screening, intelligent descriptor-based computer vision, and others, which are used as training materials. Field of interest in the study of R.H. Sianipar is multimedia security, signal processing / digital image / video, cryptography, digital communication, digital forensics, and data compression / coding. Until now, R.H. Sianipar continues to develop applications related to analysis of signal, image, and digital video, both for research purposes and for commercial purposes based on the Python programming language, MATLAB, C ++, C, VB.NET, C # .NET, R, and Java.
